To the best of our knowledge, the current widely-used attention mechanism usually ignores the influence of positions when measuring the weights of each word.

we propose a Position-Aware Attention (PAA) model which regularly decreases the weight of word i with respect to the distance between word i and word t. Supposing that we compute the context vector c t at position t, PAA first computes the weight for each word by: where f (s t−1 , h i ) is the score function which computes the weight of h i given previous decoder hidden state s t−1 and the corresponding distance.


Finally, the context vector c t is computed as a weighted sum of these encoder hidden states: We can see that PAA can tune the weights of each word according to the distance.

compared with vanilla attention, our model can pay more attention to its adjacent words given a word.


The most impressive results are that the IT-Rate of Seq2Seq4ATE is 0.02% and 0.03% which almost can be ignored compared with BiLSTM and BiL-STM+CRF.

the main reason is that Seq2Seq4AtE leverages previous label information y t−1 to decode label y t for word t. y t is compatible with y t−1 .


On one hand, different from traditional non-interactive reviews with a single sequence structure, interactive QA style reviews consist of two parallel units, i.e., question and answer.

it's rather difficult to infer the sentiment polarity towards an aspect based on a single question or single answer.


These noisy words might provide wrong signals and mislead the model into assigning a negative sentiment polarity to aspect "battery life" and vice versa.

a well-behaved approach to ASC-QA should alleviate the effects of noisy words for a specific aspect in both question and answer during model training.


(2016), we incorporate the action result o i into the inputv i of LSTM p at time-step i and compute hidden state h i ∈ R d of word x i as: where  , which could be cast as a binary classification problem.

we use a logistic function to compute where θ r = {W r ∈ R 1×4d , b r ∈ R} is the parameter to be learned.


Second, since each word x q i in question interacts all words in answer x a and vice versa, its importance can be measured as the summation of the strengths of all these interactions, i.e., matching scores computed in Eq.(7).

we perform row/column-wise summation operation over the normalized matching matrices, i.e.,α a = i S r i: .]


Multi-modality of context exists even in non-diagram questions and it requires to comprehend long lessons to obtain knowledge.

it is important to extract exact knowledge from long texts and arbitrary images.


Next, various topics and subjects in the textbooks are spread over chapters and lessons, and most of the knowledge and terminology do not overlap between chapters and subjects are split.

it is very difficult to solve problems on subjects that have not been studied before.


Moreover, a graph structure can be suitable to represent logical relations between scientific terms and to merge them with visual contexts from diagrams.

we decide to build a multi-modal context graph and obtain knowledge features from it.


Within two levels of depth, we can find "outer" node which is the opposite to "inner" in the question sentence.

our model predicts the true and false probabilities of this question as 0.464 and 0.536, respectively, and correctly solves this problem as a false statement.


Some labels are exactly the same as or are semantically close to the words in the question "What is the man wearing around his face?".

we compute the WL attention vector, that indicates how much weight we should give to each of the K objects in the image, in terms of the semantic similarity between the category labels of the objects and the words in the question.


It is intriguing whether NMT model has the ability to tackle the wrong order noises.

s, we make erroneous word order noises on English-German development set by moving one word to another position, and evaluate the drop of the translation quality of each model.


However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption.

these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware.


Advances in techniques and hardware for training deep neural networks have recently enabled impressive accuracy improvements across many fundamental NLP tasks (Bahdanau et al., 2015;Luong et al., 2015;Dozat and Manning, 2017;Vaswani et al., 2017), with the most computationally-hungry models obtaining the highest scores (Peters et al., 2018;Devlin et al., 2019;Radford et al., 2019;So et al., 2019).

training a state-of-the-art model now requires substantial computational resources which demand considerable energy, along with the associated financial and environmental costs.


For instance, rejecting behavior as not being abusive because it is outside the scope of our classification can cause substantial harm to victims (Blackwell et al., 2017), tacitly involving the NLP community in algorithmic bias that sanctions certain forms of abuse.

categorization is particularly thorny: a broad categorization is likely too computationally inefficient, yet a narrow categorization risks further marginalizing affected community members and can lead to lasting harm.


People will comply only if they accept the legitimacy of both the platform and the algorithms it employs.

abuse detection methods are needed to justify why a particular act was a violation to build legitimacy; a natural starting point for NLP in building legitimacy is recent work from explainable ML (Ribeiro et al., 2016;Lei et al., 2016;Carton et al., 2018).


For each emotional word w z k in V z e , the attention score a jk at the time step j is determined by three parts: the previous hidden state s j−1 of the decoder, the encoded representation h M of the input post, and the embedding Emb(w z k ) of the k-th word in V z e .

given the partial generated response and the input post, the more relevant an emotional word is, the more influence it will have on the emotion feature vector at the current time step.


The candidates are scored by the emotion classifier trained in advance on a dataset annotated automatically (see Section 4.1).

our model can produce the N -best candidates with more diversity, in which the one with the highest emotion score is chosen as the final result.


And it is hard to generate naturally sounding utterances in a generalizable and scalable manner.

different semantically controlled neural language generation models have been developed (Wen et al., 2015(Wen et al., , 2016aDusek and Jurcícek, 2016) to replace the traditional systems, where an explicit semantic representation (dialog act) are used to influence the RNN generation.


However, such representation impedes the cross-domain transfer between different slots and the cross-slot transfer between different values (e.g the "recommend" under restaurant domain is different from "recommend" under hospital domain).

the sample complexity can grow combinatorially as the potential dialog act space expands in large-scale real-life dialog systems, where the potential domains and actions can grow dramatically.


Graph Structure The tree-based representation cannot capture the cross-branch relationship like "restaurant-inform-location" vs. "hotel-informlocation", leading to a huge expansion of the tree.

we propose to merge the cross-branch nodes that share the same semantics to build a compact acyclic graph in the right part of Figure 4 4 .


In consequence, a single-layer disentangled self-attention with a large number of heads is difficult to handle the complexity.

we further propose to stack multiple DSA layers to better model the huge semantic space with strong compositionality.


Experimental results show that fine-tuning the pretrained BERT (Devlin et al., 2018) can lead to significantly better performance than the other models.

we will use it as the dialog act prediction model in the following experiments.


However, in real-world applications, developers can never guarantee that all possible user demands are taken into account in the design phase.

existing systems will break down when encountering unconsidered user needs.


The system is designed to guide users to find a suitable product.

when encountering unconsidered user needs such as "how to update the operating system", the system will give unreasonable responses.


Besides, the user inputs are often diverse due to the complexity of natural language.

it is impossible to collect enough training samples to cover all variants.


If the system has not seen enough instances similar to C t before, the encoding of C t will be located in an unexplored area of the dialogue embedding space.

the entropy of prior p(z|C t ) will be large.


It results in a small average JSD but the system should refuse to respond.

we add an additional criterion for the uncertainty measurement.


However, the fact that nearly all utterances contain entity information would lead to a slow model convergence.

we replace all entities with the orders in which they appear in dialogues to normalize utterances.


When IDS learns to handle new user needs in SubD5, the knowledge learnt in the training phase will be somewhat lost.

iDS needs more human intervention to re-learn the forgotten knowledge.


The main difference between our approach and others is that we introduce humans in the system loop.

we are interested in the question of how frequently humans intervene over time.


Through continuous interactions with users, the labeled data covered in the data pool will become more and more abundant.

humans are not required to intervene frequently.


In these cases, if we use all contexts indiscriminately, as in HRED, it is likely that many noises will be introduced to the model, and the generation performance will be hurt significantly.

it is critical to detect and use the relevant contexts for multi-turn dialogue generation.


However, some related contexts are far from the response in the multi-turn dialogue generation task, and the RNN-based attention model may not perform well because it usually biases to the close contexts (Hochreiter et al., 2001), namely position bias problem.

how to effectively detect and use the relevant contexts remains a challenging problem in multi-turn dialogue generation.


Furthermore, our further analysis show that the relevant contexts detected by our model are significantly coherent with humans' judgements.

we obtain the conclusion that the relevant contexts can be useful for improving the quality of multiturn dialogue generation, by using proper detection methods, such as self-attention.


ment candidates share the same underlying triple as the ground-truth next utterance, so this metric rewards highly ranked candidates that convey similar meaning and logic to the ground-truth utterance.

it can be interpreted as a more permissive version of Hits@k. Results Table 6 shows re-ranking results on the three evaluation sets (λ = 1.0, k = 10).


As a result, the budget demand for the agent decays during the course of training.

we linearly decay the parameter of the Poisson distribution so as to allocate more budget at the beginning of training.


either ungrammatical or otherwise inferior.

in our implementation, we remove clusters containing two or fewer sentences, and then sample a second candidate from each of the remaining clusters, prioritizing selecting candidates from larger clusters first.


In the dialog domain, single-reference BLEU score (Papineni et al., 2001) is sometimes used to measure response quality, but it has been shown to have little correlation with human-judged quality (Liu et al., 2016).

most works in dialog systems use human evaluation as the ultimate measure of quality (Li et al., 2016a;Sedoc et al., 2018) 8 Conclusion In this work, we perform an analysis of posttraining decoding strategies that attempt to promote diversity in conditional language models.


It computes the probability D (y|x, {c}) that the response is humangenerated given an input message x and N-best response candidates {c} = {c 1 , ...c k , ..., c N }, where denote the parameters of the discriminator.

its objective function is to minimize classification error rate: We cast the retrieval-enhanced response generation as a reinforcement learning problem to backpropagate the error computed by the discriminator to the generator via the policy gradient algorithm.


In order to capture much more information from different input sequences, VPN employs a multi-pass encoder, which contains three different encoders in order: raw-word encoder, low-level encoder and highlevel encoder.

the multi-pass encoder is able to encode more and more abstractive infor-mation from words to clusters.


To alleviate this problem, the multi-pass decoder first generates the easier (high-level and low-level) clusters from the small vocabularies, and then generates the raw words from the big vocabulary under the guide of the well-generated clusters.

the multi-pass decoder obtains significant performances.


Large part of the conversational and natural language understanding happens on the server side and then fulfilled resulting in response delays, inconsistent experience and privacy concerns.

there is a huge demand for developing on-device natural language models that work entirely on-device such as mobile phones, tablets, watches and any internet of things (IoT) devices.


The characteristics of on-device models are low memory footprint and low latency.

a direct comparison of an on-device model against cloud based neural networks might not be fair, due to the resource constraints for on-device models.


Although this heuristic approach can avoid expensive and exhausting human labeling, it suffers from noise in training data, as many negative examples are actually false negatives 1 .

when evaluating a well-trained model using human judgment, one can often observe a significant gap between training and test, as will be seen in our experiments.


The fact is that since large-scale human labeling is infeasible, D is established under simple heuristics where negative response candidates are automatically constructed (e.g., by random sampling) with a lot of noise.

advanced matching models only have sub-optimal performance in practice.


The training set of Douban is built from random sampling, while the training set of ECD is constructed through response retrieval that may contain more false negatives.

in training, Douban could be cleaner than ECD, making "hard data filtering" more effective than "soft labeling" on ECD.


Similarly, DAM gets worse when δ becomes small or large, since a smaller δ means fewer data will be involved in training, while a larger δ brings more risks to introducing noise into training.

we conclude that the teaching strategies are sensitive to the choice of hyperparameters.


We find that 20% of the conversations are completely right or a prefix of a true conversation; 58% are missing messages, 3% contain messages from other conversations, and 19% have both issues.

systems trained on the data will not be learning from accurate humanhuman dialogues.


But when directly encoding the full dialogue history along the temporal order, the model actually only focuses on the ending utterances, and earlier information is largely discarded (Li et al., 2017).

we propose a sampling-based selfsupervised network (SSN ) to account for the forgetfulness problem and solve the inconsistent order detection task.


Especially, the order signal is becoming weak as the number of dialogue turns increases.

we propose the task of inconsistent order detection to force building models to capture this signal as self-supervision explicitly.


Then we can sample multiple triples of this dialogue as utterance pair triples using the following strategies: • Ordered triple sampling: We sample a triple following the dialogue sequential order as • Misordered triple sampling: The three utterance pairs are sampled in a triple as Note that when the current dialogue length t <= 2, it is not enough to get a rational sampling for utterance pair triples.

we add three extra shared padding utterance pairs (Q −2 , A −2 ), (Q −1 , A −1 ) and (Q 0 , A 0 ) ahead of all the dialogue data before sampling 1 .


We also report some similar results in Section 4.1.

we propose a sampling-based approach to model the utterance order based on the dialogue context effectively.


Otherwise, the A t may be of low quality.

we take a two-step sampling approach to evaluate the generated utterance A t using SSN .


Crucially, A&H's model can learn multiple rules that all produce regular past tense forms, but with phonological contexts of different specificity, hence different confidence scores.

some nonce words may reside in so-called "islands of reliability" (IOR) for regular verbs: that is, there is an applicable regular rule that has a very high confidence score.


Regarding the user's familiarity with the target concept, in absence of user data we approximate it by the popularity of the target concept computed as the normalized node degree as pop(c) = log(D(c)) log(|V |−1) , where D(c) denotes the number of neighbors of concept c. Formally, given the relation s p −→ o, the accessibility scores of its endpoints s and o are computed as shown in Formula 2.

although the edges of the KG are directed, activation can flow in both directions over the same edge.


The paragraph wrap-up activation(AEoP ) is defined similarly.

aEoS s (c) = Ca (eos(s)) (c); each concept mention in the text produces three Ca scores: activation at encounter (aE), activation at the end of the sentence it occurs in (aEoS), and activation at the end of the paragraph it occurs in (aEoP ).


The recommended way of using e 1 e 2 e 3 e 4 e 5 ELMo is to concatenate ELMo (e) with GloVe (g), e.g., [g t ; e t ] (Peters et al., 2018).

the BiL-STM hidden state h t is The intuition behind SPV is that metaphoricity is identified by detecting the incongruity between a target word and its context.


Our attention mechanism does not employ non-linear transformations.

the attentive context ([ − → C ; ← − G ]) does not significantly change its colour from the context word encoding ( − → C and ← − G ).


a different part-of-speech, we can observe a grammatical change.

the sense competition for dominance may result in semantic and grammatical changes.


DiBS implements an optimal boundary setting based on the Bayes' theorem and co-occurrence statistics.

our results support previous experimental findings that infants may use such tools to acquire language.


Defining words can be obscure (Daland, 2009) and there is no crosslinguistically valid general definition of 'word' (Haspelmath, 2011).

it would make sense to also evaluate unsupervised segmentation algorithms using morpheme edges and at other definitions of wordhood (Bickel and Zúñiga, 2018).


We characterize these matrix clauses with their dependents as directed trees τ (with direction from parent to daughter nodes/clauses).

a clause will be represented by a node with out-degree equal to the number of embedded dependencies hosted by the clause.


Measurement identification uses a simple contextfree grammar along with a mapping from units to dimensions.

we know that 'inch' is a unit in the LENGTH domain which is equal to 0.02524 meters, and that "acre foot" is a unit of VOLUME equal to 1233.48 standard units (here, cubic meters).


the combination of patterns, lexiconbased evidence and paraphrasing.

for each dataset, we used the method that obtained the highest coverage.


In our work, we target for the single-round non-task-oriented short-text conversation data collected from social media platforms.

we remove tags that cannot be determined in our setting, i.e.


For level2 sentence functions, it performs the best on all metrics.

we consider the C-Seq2seq has the best performance on our test set overall.


Specifically, some annotators translate a "mostly yes" to a '3' while others translate it to a '2', and similarly for a "mostly no".

attributes that have fewer ambiguous cases (i.e., the '2' cases) tend to have a higher agreement.


Even if the initial objectives of these shared tasks differ from ours (they annotate idioms, verb-particle constructions and other non-collocation MWEs), some of the units actually intersect with the expressions we want to identify.

we rely on these corpora to initiate the construction of a multilingual corpus with dependency-based and semantic annotation of collocations.


By introducing a universal channel in our language model we reduced a representations dependence on a single language.

we can utilize an arbitrary set of languages in training an auxiliary task over UG encodings.


The general idea here is that instead of drawing λ l,m from an uninformative gamma prior as in our original model, we draw it from a prior distribution informed by the second-layer labels, as follows: where λ l ,m captures the correlation between labels at the two layers.

the information of the second-layer labels will be propagated down to the assignment process of the documents.


It is known that word embeddings are able to significantly improve topic coherence.

in this experiment, following Zhao et al.


In order to utilise the decoder after learning, we present two types of decoding functions whose inverse can be easily derived without expensive inverse calculation.

the inverse of the decoding function can serve as another encoder that produces sentence representations.


To enforce invertibility on W , a row-wise orthonormal regularisation on W is applied during learning, which leads to W W = I, where I is the identity matrix.

the inverse function is simply z = f −1 , which is easily computed.


The regularisation formula is ||W W −I|| F , where ||•|| F is the Frobe-nius norm.

1 Specifically, the update rule (Cissé et al., 2017) for the regularisation is: The usage of the decoder during training and testing is defined as follows: the decoder is also utilised after learning to serve as a linear encoder in addition to the RNN encoder.


The motivation of enforcing the invertible constraint on the decoder during learning is to make it usable and potentially helpful during testing in terms of boosting the performance of the lone RNN encoder in the encoder-decoder models (instead of ignoring the decoder part after learning).

it is important to check the necessity of the invertible constraint on the decoders.


, m r ) ∈ R p×r from the word embeddings H by applying the self-attention weights A as M = HA.

each column m k ∈ R p is a weighted linear combination of the vector embeddings h 1 , .


Although it is feasible to encode such information using positional encoding, our experiments have shown that this will lead to bad performance in our case.

we adopt a more straightforward positionwise attention, by multiplying the representation at pos by a positional attention digit a pos .


For example, words with the same form but in different domains (Sarma et al., 2018) or at different times (Hamilton et al., 2016) can have different semantic meanings.

to further improve the performance, we aim to adapt the learned neural regression function Fθ from D T to the new corpus D N .


Document classification models often use feature representations that are derived from words.

variations in word usage across time will change the distribution of features over time, which can impact the stability of document classifiers (Huang and Paul, 2018).


Popular word representations for classification train word embeddings using the context of each word (a window around the word, e.g., in skip-gram or continuous bag of words methods) (Mikolov et al., 2013;Bojanowski et al., 2016).

we seek to understand how semantic contexts of words shift across time, in addition to the words themselves.


Finally, the test results show p-value=0.076 for the frequent words and p-value=0.0038 for top feature words.

we reject the null hypothesis of top feature words at 95% confidence level while we cannot reject the null hypothesis of frequent words.


We split the data chronologically to simulate the realistic scenario where a classifier is trained on older data and tested on newer data.

the first T − 1 time domains are used for training; the last time domain is split into two equal-sized sets for development and testing.


However, for the domain divergence objective, the common part contributes positively (i.e., tries to minimize divergence) whereas the source specific part contributes negatively (i.e., tries to maximize divergence).

the common representation acquires domain independence and generalizable classification abilities while the source specific representation remains domain-specific and highly discriminating for the in-domain classification task.


Note, minimizing the domain divergence is equivalent to maximizing the loss of the domain regressor.

the associated parameters, {W , u, d}, are updated in the direction of the gradient (since we maximize with respect to them, instead of minimizing).


Moreover, a Bayes test could directly compute the probabilities of the hypotheses, which help users to make a more reasonable decision.

a Bayes test is increasingly preferred and recommended recently as an advanced tool to analyze the experimental results (Benavoli et al., 2016).


However, no explicit form of the distribution exists.

we estimate it using the Monte-Carlo simulation.


Until now, no mature and fair criterion to compare Bayes test and NHST exists.

in this study, an objective comparison between them is not provided.


Moreover, on a text corpus, certain frequency distributions over linguistic units between training and validation sets in two-fold CV intuitively possess smaller divergence than those in five-fold or ten-fold CV.

m × 2 BCV should be investigated when comparing NLP models.


However, if one discretizes the two blanks simultaneously, one might get answers like {"ice", "chips"} or {"fried", "cream"}.

we adopt an iterative algorithm which is similar to Gibbs sampling.


The Scratchpad mechanism essentially lets the decoder more easily keep track of what the model has focused on and copied from the input in the recent past, as well as what it has produced thus far as output.

scratchpad can alternatively be viewed as an external memory initialized by the input, or as an input re-encoding step that takes into account past attention and generation.


Coreference resolution is a mid-step for text understanding in downstream tasks, e.g., question answering, text summarization, and information retrieval.

generalization is an important property for coreference resolvers because downstream datasets are not necessarily from the same domain as those of coreference-annotated corpora.


For instance, in Figure 3, the root node is an NP and tag would be set to NP's acceptable tags.

among the children of the root, MINA would only process the child with an NP tag (the left child) and skip the one with the PP tag.


8 The MINA span of both of these two trees is "an extensive presence".

the gold coreference chain {"an extensive presence", "that presence"} and the system coreference chain {"an extensive presence, of course in this country", "that presence"} are equivalent if they are evaluated based on minimum spans.


Maximum spans are recoverable given the MINA spans and their corresponding parse trees.

we can use MINA spans for training and testing coreference models and then retrieve their corresponding maximum spans for evaluation.


We observe that the relation labels in RST-DT are highly imbalanced, which makes the task harder.

we experimented with a variant of our parser where we had a separate classifier for nuclearity prediction, leaving 18 labels for relation classifier instead of 39.


", the event "She was very hungry" is clearly a more reasonable choice.

using event similarity alone is too coarse to support many relevant inferences, i.e., capturing the Reason for the event, should produce a different set of relevant events, compared to Temporal (next) events To help prioritize between showing diverse types of event relations and providing a framework for this discussion, we focus on a set of discourse relations, introduced by Penn Discourse Tree Bank (PDTB) (Prasad et al., 2007).


However when complex event structures were considered, e.g., multi-argument events (Pichotta and Mooney, 2014), the large vocabulary size introduced both computational issues and ambiguity into the evaluation.

granroth-Wilding and Clark (2016) proposed a multiple-choice variation, called MCNC.


In this way, we obtain the data with which the models can learn to ask unanswerable questions by editing answerable ones with word exchanges, negations, etc.

we can generate a mass of unanswerable questions with existing large-scale machine reading comprehension datasets.


The interactions make the paragraph and question aware of each other and help to predict the answer more precisely.

we propose a pairto-sequence model, conducting attention based interactions in encoder and subsequently decoding with two series of representations.


On the other hand, the vast amount of unstructured text on the Internet can easily cover a wide range of evolving knowledge, which is commonly used for open-domain question answering .

to improve the coverage of KBs, it is straightforward to augment KB with text data.


Since the datasets for CTA transcript parsing framework are created via matching, we need to evaluate the performance of our matching methods.

for testing purpose, we manually annotated the matched text spans in transcript for 138 protocol phrases as the manual matching test set.


MOOCs, teachers need to keep a moderate length of the course to face with thousands of students with various backgrounds (Jordan, 2015), making it infeasible to manually pick out these helpful concepts.

there is a clear need to automatically identify course-related concepts, so that the students can easily acquire additional knowledge and achieve better educational outcomes.


As shown in Figure 1, we regard Heap as a course concept due to its similar structure while Binary Search Tree is a prerequisite concept of Tango Tree.

mere context information is not enough for effective expansion.


Obviously the datasets in Chinses contains a more loose M than English datasets.

when expanding concepts in Chinese courses, the new found concepts are easier to be admitted, for the radius of cluster may be too large to intercept low-quality concepts.


In another word, semantic drift is more prone to happen in the two Chinese datasets.

it is necessary to avoid semantic drifts in real concept expansion of MOOCs.


while at inference the entire sequence is generated by the resulting model on its own and hence the previous words generated by the model are fed as context.

the predicted words at training and inference are drawn from different distributions, namely, from the data distribution as opposed to the model distribution.


As the length of the ground truth sequence is |y * |, the goal of force decoding is to generate a sequence with |y * | words followed by a special end-of-sentence (EOS) symbol.

in beam search, once a candidate translation tends to end with EOS when it is shorter or longer than |y * |, we will force it to generate |y * | words, that is, • If the candidate translation gets a word distribution P j at the j-th step where j |y * | and EOS is the top first word in P j , then we select the top second word in P j as the j-th word of this candidate translation.


The objective is to maximize the probability of the ground truth sequence based on maximum likelihood estimation (MLE).

following loss function is minimized: where N is the number of sentence pairs in the training data, |y n | indicates the length of the n-th ground truth sentence, P n j refers to the predicted probability distribution at the j-th step for the n-th sentence, hence P n j y n j is the probability of generating the ground truth word y n j at the j-th step.


While the pessimistic nature of BLEU differs from human judgments and is therefore problematic, it may, in practice, pose a more substantial problem for a different reason: BLEU is difficult to optimize because it does not assign partial credit.

learning cannot hill-climb through intermediate hypotheses with high synonymy or semantic similarity, but low n-gram overlap.


This is the opposite case from BLEU, where the n-gram precision is not penalized for generating too few words.

in BLEU, a brevity penalty (BP) was introduced to penalize sentences when they are shorter than the reference.


In the SemEval STS competitions, teams create models that need to work well on domains both represented in the training data and hidden domains revealed at test time.

our model and those of Wieting and Gimpel (2018), in contrast to the best performing STS systems, do not use any manually-labeled training examples nor any other linguistic resources beyond the ParaNMT corpus (Wieting and Gimpel, 2018  our fine-tuning objective becomes: We tune γ from the set {0.2, 0.3} in our experiments.


Next, for all n-best lists, we computed all differences between scores of the hypotheses in the beam.

for a beam size of 8, this results in 28 different scores.


The evaluation was performed in the eHealth-KD corpus (Piad-Morffis et al., 2019a), using the training and development collections for training and the test collection for evaluation.

the training data is comprised of 844 sentences resulting in a total of 9540 annotations among key phrases and relations.


Finally, by modifying the grammar, this strategy can be extensible to other machine learning challenges.

we plan to explore this line of research in the future, to compare our proposal with other AutoML frameworks in standard benchmarks.


Following previous work (Nguyen and Grishman, 2015; Liu et al., 2018a), given an instance x, we embed each token t i as t where p w is its word embedding, p p is its position embedding, and p e is its entity tag embedding.

t 0 is the representation of trigger candidate.


By this means, we can unify the optimized directions of these components.

the loss function of our lexical-adversarial ∆-learning is: Following Liu et al.


(1) The model uses a lattice-based structure to dynamically integrate word-level features into the character-based method.

it can leverage multi-granularity information of inputs without suffering from segmentation errors.


Current cell state c j records all historical information flow up to the current time.

the character-based LSTM functions are: where σ() means the sigmoid function.


Note that here e can have a type that is very different than the type of M aui, for example e can be Oprah Winfrey in which case it's type would be Actor but using the neighborhood we can still project it to be close to USA for the query.

we propose A2N, an effective model (Section 2) which, conditioned on the query, uses a bi-linear attention on the graph neighborhood of an entity to generate an embedding representation of the entity.


From this example, we also see that the syntactic and semantic information can complement each other to both promote the important context words and blur the irrelevant words.

we argue that the syntactic and semantic information should be allowed to interact earlier in the modeling process to produce more effective representations for EFP.


Unfortunately, such parser is not always available in different domains and languages.

in the future work, we plan to develop methods that can automatically induce the sentence structures for EFP.


4 Note that only a fraction of examples in the MATRES dataset contain distinct time expressions 4 Augmenting word embeddings with time embeddings increases the number of network parameters; however, additional experiments revealed that increasing the size of the GloVe embeddings in the basic temporal model did not lead to an improvement in performance.

it does not seem that extra parameters in the model contribute to the observed improvements.


5 In MATRES, only a fraction of the examples contain time expressions and are consequently affected by inclusion of time embeddings.

to test the full potential of the proposed approach, we additionally collect a test dataset of examples with explicit timexes that expose their temporal relation; we view the timexes as distant supervision for the event pairs.


However, our aim is to improve the performance of the event ordering model on moderately sized datasets, where the knowledge induction from timex embeddings play a larger role.

we report results on training sets of size 2000, 3000, and 4000 samples.


π is the policy of the agent, and MLP is a multilayer perceptron with three layers with ReLU activation functions.

eMR-biGRU learns the general importance of each memory entry in relation to its neighbors rather than independently computing the importance of each entry with respect to the query, which is useful when selecting out the most important entries among highly similar data instances (e.g.


In addition to the video frames, the dataset also provides subtitles for each video frame.

solving the questions requires compositional reasoning capability over both a large number of images and texts.


This is because TVQA is composed of consecutive image frames captured within a short time interval, which tend to contain redundant information.

the value network of the actor-critic model fails to estimate good value of the given state since deleting a good frame will not result in the loss of QA accuracy.


Thus the value network of the actor-critic model fails to estimate good value of the given state since deleting a good frame will not result in the loss of QA accuracy.

we compute the reward R (t) as the accuracy difference between at time step t and t − 1 then use only the policy with non-episodic REINFORCE for training.


Intuitively, if we construct a dataset for NLSM by randomly sampling some pairs of sentences, the resulting dataset would be extremely imbalanced, where the most of the pairs are neutral for NLI or not duplicated for STS.

to make the dataset relatively balanced, a sampling strategy is often required.


For QuoraQP dataset, the providers state that "Our original sampling method returned an imbalanced dataset with many more true examples of duplicate pairs than non-duplicates.

we supplemented the dataset with negative examples.


In contrast to other entailment datasets mentioned previously, the hypotheses in SciTail are created from science questions while the corresponding answer candidates and premises come from relevant web sentences retrieved from a large corpus.

these sentences are linguistically challenging and the lexical similarity of premise and hypothesis is often high, thus making SciTail particularly difficult.


As shown in Table 1, neither of the two datasets were annotated with query labels.

to make the tasks feasible, SCONE (Long et al., 2016) and SequentialQA (Iyyer et al., 2017) exclude many questions with rich semantic and contextual types.


In order to study more diverse information needs, we also included some easy examples (end up with using 12.9% of the easy examples in Spider).

4,437 questions were selected as the interaction goals for 200 databases.


In order to apply standard DMD (Schmid, 2010) to such a signal, the first-order Koopman assumption is employed, which can be written in multiple different forms: where k = 2, ..., N and A is a m × m square matrix.

the assumption is that each sentence has words in it which lie in a constant subspace generated by A (Brunton et al., 2016), or that the words in a sentence transition from one another smoothly, transformed by the constant operator A.


Furthermore, by corresponding with other researchers, we have learned that previous papers on AMR parsing report SMATCH scores using differing values of r. Another problem is that SMATCH maps one node to another regardless of their actual content, and it only considers edge labels when comparing two edges.

two different edges, such as "ask-01 :ARG1 leave-11" and "make-01 :ARG1 pie" in Figure 1, can be considered identical by SMATCH.


Generally, more high-order matches indicate better parsing performance, but sometimes we want to give partial credit for distinguishing partially correct results from the fully wrong ones.

combining SMATCH with SEMBLEU may give more comprehensive judgment.


On the other hand, we observe the reconstruction model could potentially go beyond surface token-wise match between NL utterances and MRs, promoting more complex (longer) candidate MRs that more adequately encode the semantics of the input utterance 7 (e.g., in Example 3, z 5 receives a much higher reconstruction score, while the difference between the discriminative matching scores is small).

on the challenging CONALA dataset with much weaker alignment between its succinct NL utterances and highly compositional MRs (e.g., Example 4), the matching model does not function as well (e.g., the incorrect MR z 1 received a higher matching score).


Work on parsing to SQL (Zhong et al., 2017;Iyer et al., 2017;Finegan-Dollak et al., 2018;Yu et al., 2018a) has either involved simple DBs that contain just one table, or had a single DB that is observed at both training and test time.

modeling the schema structure received little attention.


The score s glob j is computed with a feed-forward network, and the score s loc j is computed for all legal schema items by multiplying the matrix S link ∈ R V legal ×|x| , which contains the relevant linking scores s link (v, x i ), with the attention vector a j .

decoding unseen schema items is done by first attending to the question words, which are linked to the schema items.


We find that for all graphbanks, just knowing the edge label is enough to group an edge properly.

we manually decide for each of the 216 edge labels of all graphbanks whether the edges with this label are to be grouped with their target or source node.


For patterns that are the same across all graphbanks, such as the 'triangle' created by the control verb "want" in Figures 1 and 2, we can re-use the same pattern as for AMR.

control verbs are identified automatically, and their sources are assigned annotations which enforce the appropriate argument sharing.


We delete R-HNDL and L-HNDL edges only if this does not make the graph disconnected.

we need heuristics for them (see table 5).


Second, even for the perfectly aligned sentences, the oracle action sequence is not the only or the best action sequence that can lead to the gold graph; there could be shorter sequences that are easier to learn.

strictly binding the training objective to the oracle action sequences can lead to suboptimal performance, as evidenced in (Daumé III and Marcu, 2005;Daumé III et al., 2009;Nivre, 2012, 2013;Ballesteros et al., 2016) among others.


More specifically, our paper makes the following contributions: (1) We curate a new dataset, MUStARD, for multimodal sarcasm research with high-quality annotations, including both mutlimodal and conversational context features; (2) We exemplify various scenarios where incongruity in sarcasm is evident across different modalities, thus stressing the role of multimodal approaches to solve this problem; (3) We introduce several baselines and show that multimodal models are significantly more effective when compared to their unimodal variants; and (4) We also provide preceding turns in the dialogue which act as context information.

we surmise that this property of MUStARD leads to a new sub-task for future work: sarcasm detection in conversational context.


We extend the definition of an utterance 2 to include consecutive multi-sentence dialogues of the same speaker to prioritize completeness of information.

61.3% of the utterances from the dataset are single sentences, while the remaining utterances consist of two or more sentences.


This is similar to the behavior of hidden Markov models.

in a parse tree each non-leaf node is a sentiment polarity and is connected to exactly one leaf node which is a string of terminals.


However, the target domain corpus distribution is usually different(movie or product review in our case).

we fine-tune each source language model on the target corpus (without labels) for a few iterations before training each classifier.


Table 6 shows slightly lower performance for BCN+ELMo and ULMFiT while BERT performed much worse.

for effective sentiment analysis, a large corpus is required for pre-training BERT.


Political debates offer a rare opportunity for citizens to compare the candidates' positions on the most controversial topics of the campaign.

they represent a natural application scenario for Argument Mining.


It is not until we add in the possible alignments that the percentage of four-word or longer phrases grows to 24% in the MSR RTE test set; when we look only at sentence pairs containing a least one possible alignment, the percentage of longer phrases grows to 44%.

evaluating only on the 406 sentence pairs that contain at least one possible requires systems not only to perform well on longer alignments, but also to avoid sacrificing performance on short alignments.


In the first sentence of Figure 7, for example, there is a potential chunk boundary between "she repaid me by hooking up with" and "my boyfriend" (because "my boyfriend" is itself a constituent).

there is a chunking where "my boyfriend" is its own, separate chunk, and in the preliminary alignment for that chunking, the pointer-network must have assigned "my boyfriend" a lower score than it did the rest of the chunks.


Being constrained to respect chunk boundaries did lower the humans' precision, but increased their recall and overall performance.

we conclude that incorrect phrase chunk boundaries is not so grave a concern.


This is essential as the entailment scores will be used to predict improved link prediction scores on the test set.

the comparison will not be valid if the method has access to the test triples while computing entailment scores.


It assumes that a data point x is generated by the following random process: (1) z is sampled from p(z) and 2x is generated from p θ (x|z).

the natural training objective would be to directly maximize the marginal log-likelihood log p θ (x) = log z p θ (x|z)p(z)dz.


We also introduce a constraint on z that keeps the distributions of z t (the latent content variable obtained by encoding the generated sequence x t ) and z s close: In other words, it pushes the generated sequence x t to be in a similar semantic space with the ground-truth target sequence x t .

it can help alleviate the generator collapse problem where a generator produces only a handful of simple neutral patterns independent of the input sequence, by relating z t to z t .


Despite this, certain entities are so central to the text that they are mentioned nearly everywhere and are thus no especially informative.

we filter out these major mentions and encode as a binary feature whether or not the sentences contain the events share a non-major mention.


For Glavaš andŠnajder, we consider only their coherent model, which is the best model that does not use the gold coreference relations.

in Table 5, the reported result of all models are the average of both classes (PC and CP).


Note that, this combined multiple domain task clearly entails a large set of answers and complex search space in comparison to the individual domain tasks.

the existing multi-domain semantic parsing models (Herzig and Berant, 2017) fail when trained under weak supervision setting.


, m} and ∀v ∈ V. The student takes the probability outputs from the experts as the ground truth and is trained in a supervised manner to minimize the cross-entropy loss L w.r.t to teachers' probability distribution: where {θ k } K k=1 are the policy parameters of experts and θ S are the student model parameters; similarly p S θ (z ij = v; x k , z i{1:j−1} ) is the probability assigned to output token z ij by student S. This training objective enables the unified parser to learn domain-specific parsing strategies from individual domains as well as leverage structural variations across domains.

the combined multi-domain policy S is refined and compressed during the distillation process thus rendering it to be more effective in parsing for each of the domains.


Note that for CALENDAR domain, WEAK-INDEPENDENT is unable to learn the parsing policy probably due to the complexity of temporal utterances.

further distillation on the inaccurate policy leads to drop in performance.


We observe that due to weak signal strength and enlarged search space from multiple domains, WEAK-COMBINED model performs poorly across domains.

further reinforcing the need for the distillation process.


For example, "谢谢大家点开这 篇文章" (Thanks for opening this article).

we extract the keywords of the article which serve as the topics of the news.


We assume that this is because even with the hierarchical structure, this model can not understand the long input well.

it can not extract the main point of the story and generate general comments.


The maximum achievable performance by a model that predicts spans from the context is 97.8 F1 score.

we find the extractive spans from the passage which have the maximum F1 score with answers and treat them as answers for our answer position embedding.


(2019) find that in CoQA dataset, most questions in a conversation have a limited dependency within a bound of two turns.

we choose the number of history turns as n = 3 to ensure the target questions have enough conversation history information to generate and avoid introducing too much noise from all turns of QA pairs.


Manually curating a dataset of comparable size for a new language will be tedious and expensive.

it would be desirable to leverage existing question answering datasets to help build QG models for a Sentence : िव�ा के ये सभी �प हमारे रा�ट् रीय �ान के िविवध अं ग ह� (All these forms of education are diverse aspects of our national knowledge system.)


Formally, the reconstruction loss is defined as Reconstruction Reward One-to-one transfer (e.g., {delicious↔bland, caring↔unconcerned}) is usually preferable to many-to-one transfer (e.g., {delicious→bad, caring→bad}).

we introduce a reconstruction reward for Rep φ 3 to encourage one-to-one transfer, i.e., where rec is the reconstruction loss in Eq.


From this perspective, Figure 3 shows that if we stop earlier, i.e., when the current style is closer to the source style, more content will be preserved and more weakly stylized words may be kept.

controllable trade-off is achieved by manually setting p stop .


CWQ (Talmor and Berant, 2018c): Crowdsourcing workers were shown compositional formal queries against Freebase and were asked to re-phrase them in natural language.


Pre-training on a source RC dataset and transferring to the target improves performance by 21% on average for DOCQA (improving on 8 out of 11 datasets), and by 7% on average for BERTQA (improving on 10 out of 11 datasets).

pre-training on a related RC dataset helps even given representations from a model like BERTQA.


Average performance across datasets in Table 4, when choosing the best source dataset from Table 2, is 38.9 (DOCQA) and 43.5 (BERTQA).

one can select a dataset to pre-train on based on generalization performance and suffer a minimal hit in accuracy, without fine-tuning on each dataset.


Pertaining to the models benchmarked by us, we found that our re-implementation of ASR (Ours) leaves a lot to be desired.

our proposed IAL-CPG model almost doubles the score on all metrics compared to ASR (Ours).


In (4), we remove the pointer generator (PG) completely.

there is also no curriculum reading in this setting.


This method cannot be directly applied to QA, because the same fact can be highly relevant for one query and irrelevant for another.

we constrain the computation of loglikelihood differences to a single data sample (i.e., a single query).


One cause is documents with 終値 "closing price"; this has few market-based English neighbors (Table 3).

the model fails to transfer across languages.


Omitting modularity hurts accuracy prediction on cross-lingual document classification substantially, while omitting the other three measures has smaller effects ( Figure 5).

modularity complements the other measures and is more predictive of classification accuracy.


This work focuses on modularity as a diagnostic tool: it is cheap and effective at discovering which embeddings are likely to falter on downstream tasks.

practitioners should consider including it as a metric for evaluating the quality of their embeddings.


Nevertheless, this representation will have very low modularity.

while modularity can identify bad embeddings, once vectors are well mixed, this metric-unlike QVEC or QVEC-CCA-cannot identify whether the meanings make sense.


Recall that the char-RNNLM has no notion of words, whereas the number of BPE units increases with |V | (indeed, many whole words are BPE units, because we do many merges but BPE stops at word boundaries).

one interpretation is that the Bible corpora are too small to fit the parameters for all the units needed in large-vocabulary languages.


As described in the question-answer writing process, the answers in our dataset are different from those in some existing extractive datasets.

we consider the task of answer generation for TWEETQA and we use several standard metrics for natural language generation to evaluate QA systems on our dataset, namely we consider BLEU-1 5 (Papineni et al., 2002), Meteor (Denkowski and Lavie, 2011) and Rouge-L (Lin, 2004) in this paper.


The first is paraphrasing, which is not surprising because that a well pretrained language model is expected to be able to better encode sentences.

the derived embedding space could work better for sentence comparison.


The model uses a neural-network that takes sentences as input and generate a 4800 dimension embedding for each sentence.

on our dataset, for each post in Twitter conversations, we get a 4800 dimension vector.


However, this results in changing the structure of trees.

we only used balancing on original conversation trees for stance classification and not for rumor classification on BCTrees.


2) Prior work on this dataset used F1-score .

the use of this measure allows to compare with prior research.


However, the generated summaries of these models can not be controlled (i.e., their meanings can be quite different from the original and contents can be repeated).

copy mechanism (Gu et al., 2016), coverage model (See et al., 2017) and reinforcement learning model optimizing ROUGE (Paulus et al., 2017) are introduced.


HIBERT aims to learn the representation of a document, where its basic units are sentences.

a natural way of pre-training a document level model (e.g., HIBERT) is to predict a sentence (or sentences) instead of a word (or words).


Aside from the difficulties in obtaining training data, a major obstacle to the application of end-to-end models to multi-document summarization is the sheer size and number of source documents which can be very large.

it is practically infeasible (given memory limitations of current hardware) to train a model which encodes all of them into vectors and subsequently generates a summary from them.


The intuition behind this transformation is the fact that machine learning systems tend to require a sufficient number of training samples prior to producing accurate predictions.

low-frequency terms should ideally be replaced by respective high-frequency hypernyms that semantically convey the original meaning.


However, the NEG strategy exhibits its best performance at θ f = 500 on the Gigaword dataset and at θ f = 1000 on the DUC 2004 dataset, with the exception of the ROUGE-2 metric which is maximized at θ f = 500.

the LG strategy seems to be more fit in improving the performance of the deep learning system when generalizing low-frequency words.


LexRank is a graph-based extractive summarizer often used as a baseline.

most current and future summarization systems should perform better and should be covered by the selected scoring range.


Then, for a topic T from the dataset (W), we select a summary s and ask: among the summaries which are better than s for one metric (N), how many are better for all metrics (F)?

this is given by: Here, m(x) is the score of the summary x according to the metric m. F N measures the difficulty of finding consistent improvements across metrics.


When the baseline scores go up, the disagreement between metrics is strong enough that we cannot identify summaries which are considered better than the baseline for each metric.

there is no common trend between metrics that can be exploited by reporting them together.


Also, the high-scoring range covers 38% of the full scoring range (from LexRank to upper-bound), while human judgments cover 35% of the full scoring range.

the width of the scoring range and the population size do not explain the As a limitation of this study, we can note that the data generation procedure simulates further progress in summarization by stochastically optimizing each evaluation metric.


We take the correct notion of expectedness to be the expectedness of the word form treated as if it were the first instance of that lexeme which had been observed.

we base our measures of regularity on the conditional probability of a word type w given the rest of the forms in the language with the target lexeme removed.


Recently, several authors have questioned the received wisdom that irregularity and frequency are related (Yang, 2016;Fratini et al., 2014).

5 it has become important to test this relationship empirically.


A nested-model likelihood-ratio χ-squared test shows that the log count factor is significant with p < 0.001.

the correlation between irregularity and frequency is considerably more robust when considered at the lexeme level.


The advantage for training with visual context when testing without is robust across different languages (English, German and Spanish) and different models (GRU, LSTM, ∆-RNN, as well as those that use BERT embeddings).

language models perform better when they learn like a baby, i.e, in a multi-modal environment.


Moreover, in our study we focused on neural estimates of action verbs irrespective of their specific objects.

the LPG and LIFG may more closely reflect action-semantic variance and show a greater modulatory effect of negation.


This more closely aligns with the idea that some dimensions of the affirmative representation are being processed while others reduced, possibly due to competing semantic alternatives.

future work should also investigate whether modeling negation as a set of alternative meanings can further show the impact of negation on semantic representation in the brain.


Sequence-processing neural networks led to remarkable progress on many NLP tasks.

there has been increasing interest in understanding to what extent they process language as humans do.


Compared with region-based approaches, our ARNs detect mentions by exploiting head-driven phrase structures, rather than exhaustive classifying over subsequences.

aRNs can significantly reduce the size of candidate mentions and lead to much lower time complexity.


And α → +∞ means that Bag Loss will only choose the word with highest P (c i |x i ) as anchor word, while all other words in the same bag will be regarded as NIL.

bag Loss guarantees that at least one anchor word (the one with highest P (c i |x i ), and its corresponding w i will be 1.0) will be selected for each bag.


We find that this is because our model can predict anchor word in a very sharp probability distribution, so slight change of α does not make a big difference.

in all our Most Frequent Anchor Words PER I, you, he, they, we, people, president, Mandela, family, officials ORG government, Apple, they, its, Nokia, company, Microsoft, military, party, bank FAC building, home, prison, house, store, factories, factory, school, streets, there GPE country, China, U.S., US, Cyprus, our, state, countries, Syria, Russia LOC world, moon, areas, space, European, Europe, area, region, places, border NIL the, a, of, 's, in, and, to, his, who, former  experiments we empirically set α = 1 without special declaration.


The set of 77 candidate TDM triples comes from the training data, and many of these contain datasets that appear only after 2015.

fewer papers are tagged with these triples.


Consequently, fewer papers are tagged with these triples.

for evaluation we manually choose ten TDM triples among all TDM triples with at least ten associated papers.


Previous NER tagging models have to introduce one set of entity tags (e.g., BIO tags) for each attribute.

the large attribute size in reality renders previous works an infeasible choice to model attribute extraction.


For example, the title of a 'dress' contains 'long dresses', the word 'long' may be tagged as values for attributes Sleeve Length and Dresses Length simultaneously.

we randomly sampled 1, 500 triples from AE-650k for manual evaluation and the accuracy of automatic labeling is 95.6%.


CopyRNN does not model the one-to-many relationship between the document and keyphrases.

keyphrase generation only depends on the source document and ignores constraints on the correlation among keyphrases.


As mentioned in the work (Meng et al., 2017), the Seq2Seq models can predict absent keyphrases.

we only compare our method with CopyRNN and ConNet, and evaluate the performance within the recall of the top-10 and top-50 results to see how many absent keyphrases can be correctly predicted.


The fact that this data is up-to-date and is generated by patients overcomes the weaknesses of traditional ADR surveillance techniques (Leaman et al., 2010).

social media could complement traditional information sources for more effective pharmacovigilance studies, as well as potentially serve as an early warning system for unknown ADR, which may be important for a clinical decision.


All quantities are explicitly given in the problem text.

the N node is used to tag a word token if and only if the token represents a quantity.


Besides, even if the model accurately predicts all true labels, it still may result in an unreasonable training loss due to the inconsistent order with the pre-defined label sequence 3 .

in this work, we propose a simple but effective sequence-to-set model, which aims at alleviating the dependence of the model on the label order.


However, in real-scenarios, there are other common label distributions, e.g., uniform distribution (Lin et al., 2018a).

here we analyze the universality of the Seq2Set model, which means that it can achieve stable improvements in performance under different label distributions.


From the left part of Figure 5, we can see that after the first iteration, the model considers the word operetta itself alone is likely to be an ob-  ject name, probably because the following word for is usually a context word being annotated as O.

it tends to route word for to both the slot O and the slot I-object name.


if the governor of r is the aspect word then 7: else 9: A.add(term) 13: end if 14: end for We still do not know the precision of the rule candidates obtained with Algorithm 1.

in the second part of our rule mining algorithm, for each rule candidate, we use it to extract aspect terms from another annotated set of review sentences (a validation set) and use the result to estimate its precision.


Among the four versions of RINANTE, RINANTE-Double-Pre yields the best performance on SE14-L and SE15-R, while RINANTE-Shared-Alt is slightly better on SE14-R.

we think that for exploiting the results of the mined rules, using two separated BiLSTM layers for aspect terms and opinion terms works more stably than using a shared BiLSTM layer.


A higher C(y i , y j ; x) is assigned if y i and y j is a more confusing type pair (i.e., more easily mislabeled by the current model).

the costsensitive regularizer will make the training procedure pay more attention to distinguish between confusing type pairs because they have larger impact on the training loss.


(2017), the probability P (y j |x i ; θ) of classifying instance x i into the wrong class y j can be directly regarded as the mislabeling risk of that instance.

our instance-level estimator is: Then cost-sensitive regularizer for each training instance can be written as: Note that if the probability of making correct prediction (i.e., P (y achieves its minimum when the probabilities of mislabeling x i into all incorrect classes are equal.


In fact, external corpus can also be exploited by pre-trained language models to generate sentences.

we turn to pre-trained language models, attempting to leverage their knowledge learned from the large-scale corpus for event generation.


Since the prediction is separated with roles, an argument can play multiple roles, and a token can belong to different arguments.

the roles overlap problem can also be solved.


It is evident that adjunct tokens can adjust the smooth and diversity of expression.

we try to rewrite them to expand the diversity of the generation results, while keeping the trigger and arguments unchanged.


It is based on the following two considerations: one is that two arguments that play the same role may diverge significantly in semantics; another is that the role an argument plays is largely dependent on its context.

we should choose arguments that are semantically similar and coherent with the context.


Adjunct Token Rewriting The results of argument replacement can already be considered as the generated data, but the constant context may increase the risk of overfitting.

to smooth  Figure 4: Flow chart of the generation approach.


However, not all of them are valuable for the extractor and some may even degrade its performance.

we add an extra stage to quantify the quality of each generated sample to pick out those valuable.


In addition, although our generation method can control the number of generated samples and filter with quality, it still suffers the deviation of roles alike with distant supervision.

for the future work, we will incorporate relation between events and relation between arguments into pre-trained language models, and take effective measures to overcome the deviation problem of roles in the generation.


In other words, instead of modeling the joint probability of the input sentence and its dependency parse as in a generative model, we model the conditional probability of the sentence and parse given global information of the sentence.

our approach breaks the context-free assumption in a similar way to discriminative approaches, while it is still able to utilize many previous techniques (e.g., initialization and regularization techniques) of generative approaches.


We use a trivial heuristic during decoding, matching labels of consecutive words by order only.

an I-or L-label is merged with a preceding B-or I-if they appear on the same position in neighboring multilabels and have the same type.


In Nature Language Processing (NLP), words contribute differently to different tasks.

attention-based models pay more attention on important words than unimportant words.


However, in NLP, the minimum unit is not word but sense.

nLP tasks need a noise reduction method at a finer granularity than attention mechanism.


Considering REINFORCE method is used in training, a novel method is introduced to reduce the variance of rewards.

our model outperforms the baseline by a clear margin and the ablation analysis proves the effectiveness of our method.


Primary dialogue systems (Vinyals and Le, 2015;Shang et al., 2015) try to mimic human beings to generate fluent utterances, whereas paying little attention to the intrinsic factors of human conversations: exchanging information and enhancing interaction .

they are prone to generate dull and generic responses.


By minimizing NLL, the supervision signals in D may not sufficiently flow to GTMN, as GTMN is nested within response decoding.

besides NLL, we propose a state update loss that directly supervises the learning of GTMN with D. The idea is to minimize the distance between the ground truth status of meta-word expression and the status stored in the state memory panel.


In the first example, the full model gradually reduces the distance between the value and the goal of copy ratio expression with the generation process moving on.

it just copies "pull the ring out" from the message, which makes the response informative and coherent.


Compared with realvalued attributes, categorical attributes are easier to express.

both the full model and GTMNES2S w/o SU successfully generate a response with multiple utterances, although the distance between the value and the goal of MU expression in GTMNES2S w/o SU is still in a mess.


Figure 1 illustrates an example human discussion, where information scattered in separate paragraphs must be consolidated to com-pose grounded and appropriate responses.

the challenge is to connect the dots across different pieces of information in much the same way that machine reading comprehension (MRC) systems tie together multiple text segments to provide a unified and factual answer (Seo et al., 2017, etc.).


Unlike passages, where sentences are often the default semantic modeling unit, in multi-turn conversations, a turn is a topically coherent unit embodied with immediately relevant context, making it a linguistically intuitive segment for computationally modeling verbal interactions.

in this work, we propose a hierarchical attention neural network architecture, combining turnlevel and word-level attention mechanisms, to improve spoken dialogue comprehension performance.


In multi-turn dialogues, each turn is one round of information exchange between speakers, thus making it a linguistically intuitive segment for modeling verbal communications.

we postulate that for spoken dialogue comprehension, it is more effective to model conversations turn by turn using a multi-granularity design.


ID subnet: Unlike the Slot-First mode, the reinforce vector r is acquired by the hidden states and the context vectors of BLSTM.

(4) (5) (6) can be replaced by: The intent reinforce vector r inte is still defined by (7), and it is fed to the SF subnet.


For the sequence labeling task, it is beneficial to consider the correlations between the labels in neighborhoods.

we add the CRF layer above the SF subnet outputs to jointly decode the best chain of labels of the utterance.


As we can Figure 3: Effect of iteration number on the model performance in SF-First mode see, both models including only the SF subnet or the ID subnet have acheived better results than the BLSTM model.

we believe that both SF subnet and ID subnet have significance in performance improvement.


Such constraint optimization problem could be solved by introducing Lagrange multiplier to incorporate the constraint: where λ x→y and λ y→x are the Lagrange parameters and the constraint is formulated as follows: Now the entire objective could be viewed as the standard supervised learning with an additional regularization term considering the duality between tasks.

the learning scheme is to learn the models by minimizing the weighted combination of an original loss term and a regularization term.


The model architectures for NLG and NLU are a gated recurrent unit (GRU) (Cho et al., 2014) with two identical fully-connected layers at the two ends of GRU.

the model is symmetrical and may have semantic frame representation as initial and final hidden states and sentences as the sequential input.


The model predicts the slot-value label in a non-parametric way based on a certain metric, which enables the model architecture not to structurally depend on domains and slot-types.

a single SUMBT can deal with any pair of domain-slot-type and slot-value, and also can utilize shared knowledge among multiple domains and slots.


The reason is that softmax loss only focuses on whether the sample is correctly classi- fied, and does not require intra-class compactness and inter-class separation.

we replace softmax loss with margin loss to learn more discriminative deep features.


Multiple GCN layers can be stacked on top of each other.

a vertex representation in a GCN with k layers is influenced by all vertices at a maximum distance of k in the graph.


Ω(b) = k b k log b k , solving the penalized one-hot-argmax has a closed form solution that can be computed using the softmax function (Boyd and Vandenberghe, 2004), that is: .

we replace the non-differentiable one-hot-argmax operation in Algorithm 1 with a softmax in order to build a smooth and fully differentiable surrogate of the parsing algorithm.


the same vector is used as input for every token).

7 the GCN is forced to rely on tree information alone (see App.


Though balanced trees are natural with bottom-up computation of TreeLSTMs, for GCNs they would result in embedding essentially random subsets of words.

we compare only to left-to-right chains of dependencies.


Many standard idioms are present in our work compared with the traditional machine translation.

the translated character sequences in this layer are further mapped into the standard idioms in the idiom set (i.e., * * in Figure 1).


However, machine learning algorithms, which detect and exploit recurring patterns in large datasets by design, can just as easily use artifacts as real linguistic clues.

models trained on these datasets can achieve high test accuracy by exploiting artifacts but fail to generalize, e.g., they fail under adversarial evaluation (Jia and Liang, 2017;Ribeiro et al., 2018).


Every hypothesis occurs three times in the dataset, each time with a unique label and premise combination (not shown in this table).

a hypothesis-only baseline will only achieve chance accuracy, but a full-input model can trivially solve the dataset.


Second, for a sentence, one needs to replace multiple words instead of a single word, making the number of possible sentences after augmentation increases exponentially.

these methods often need to augment one sentence multiple times and each time replace a different subset of words in the original sentence with different candidate words in the vocabulary; even doing so they still cannot guarantee adequate variations of augmented sentences.


In this way we can model the probability of a class as Bernoulli's distribution which makes the probability of each class c j independent from the other class probabilities.

we use a Softmax dense layer for multi-class and Sigmoid dense layer for multilabel classification to get the probabilities associated with each target class.


As unfluent adversarial examples are not in the manifold of real sentences, the victim model only needs to adjust its decision boundary out of the sentence manifold to fit them.

fluent adversarial examples may be more effective than unfluent ones.


On the contrary, if our model produces the same values for the attributes, but it tells that A is a "BAD" product, we then think the model gives bad explanations.

for a given classification prediction made by the model, we would like to explore more on the fine-grained information that can explain why it comes to such a decision for the current example.


And we define Explanation Factor EF (S) as the semantic distance of input texts, generated explanations and golden explanations.

the objective function of MRT can be further formalized as: MRT exploits EF (S) to measure the loss, which learns to optimize GEF with respect to the specific evaluation metrics of the task.


As aforementioned, we have an assumption that if we use fine-grained explanations for classifica-  tion, we shall get better results than using the original input texts.

we list the performance of the classifier C in Table 5 to make the comparison.


Such modifications might flip words either to a different word in the vocabulary or, more often, to the out-of-vocabulary token UNK .

adversarial edits can degrade a word-level model by transforming the informative words to UNK .


Empirically, we find that the background model (by itself) is less accurate, because of the large number of words it is trained to predict.

it is best to train a precise foreground model on an in-domain corpus and focus on frequent words, and then to resort to a general-purpose background model for rare and unobserved words.


Intuitively, we expect a high value of S A W,V to lead to a lower robustness of the downstream classifier, since the adversary has more degrees of freedom to attack the classifier.

when using word recognition as a defense, it is prudent to design a low sensitivity system with a low error rate.


This can be framed as the softmax output if taking a probabilistic treatment: In this way, the model only sees and learns headattaching decisions for each individual words.

the model is unaware of the global tree structures and may assign probabilities to non-tree cyclic structures, which are illegal outputs for dependency parsing.


The key difference from previous work in pre-dicting MISC labels is that we are restricting the input to the real-time setting.

models can only use the dialogue history to predict the label, and in particular, we can not use models such as a conditional random field or a bi-directional LSTM that need both past and future inputs.


If the current turn has multiple keywords, the corresponding multiple K-dimension kernel features are first summed up before feeding to the dense layer.

the intermediate kernel layer serves as a soft aggregation mechanism to account for multiple-to-one keyword transition.


seq2seq architectures are not suited to deal with the ambiguous nature of dialogs since they are inherently deterministic, meaning that once trained they cannot output different sequences to the same input.

they tend to produce boring and generic responses (Li et al., 2016a;Shao et al., 2017;Zhang et al., 2018a;.


Computing the unigram and bigram KL divergence with a uniform distribution instead of the model yields a value of 4.35 and 1.87, respectively.

all models learned a much better distribution, suggesting that this is indeed a useful metric.


Our approach focuses on creating sense-level embeddings with full-coverage of WordNet, and without recourse to explicit knowledge of sense distributions or task-specific modelling.

a simple Nearest Neighbors (k-NN) method using our representations is able to consistently surpass the performance of previous systems using powerful neural sequencing models.


We leverage contextual embeddings, semantic networks and glosses to achieve fullcoverage of all WordNet senses.

we're able to perform WSD with a simple 1-NN, without recourse to MFS fallbacks or task-specific modelling.


This is necessary to bias the final inference towards the senses that the target word has higher weights on.

the loss function on which we run MWU with starting point where the second term is the KL divergence between two distributions scaled by a hyperparameter λ.


On the other hand, the highquality embedding of a word (MWE) is also supposed to accurately represent the meaning of the word (MWE).

we believe that the better the embedding is, the better sememes it can predict.


Pairwise comparisons can be used to infer rankings or ratings by assuming a random utility model (Thurstone, 1927), meaning that the annotator chooses an instance with probability p, where p is a function of the utility of the instance.

when instances in a pair have similar utilities, the annotator selects one with a probability close to 0.5, while for instances with very different utilities, the instance with higher utility will be chosen consistently.


tasks with visibly lower correlations: SentType and the two downstream tasks.

for these tasks, the relative performance of individual models differs more between languages.


Our method is agnostic to the word embedding model.

we experiment with two popular similar embedding models: (i) SkipGram (henceforth SKIP) (Mikolov et al., 2013), and (ii) Structured SkipGram (henceforth SSKIP) (Ling et al., 2015).


We do further analysis by counting the number k of unique S-classes in the top 5 nearest neighbors for word embeddings; k is 1.42 times larger for embeddings of dimensionality 400 than 200.

more dimensions results in more diverse neighborhoods and more randomness.


Compared to the WIKI-PSE dataset, this subset has fewer (13,000 vs. 44,250) training examples, and a larger number of labels per example (2.67 vs. 1.75).


3 However, mere lists of collocations are often insufficient for both L2 acquisition and NLP.

a language learner may not know the difference between, e.g., come to fruition and bring to fruition or between have [an] approach and take [an] approach, etc.


Both are noun-adjective combinations and both are used as intensifiers, but they subtly differ in that only one enforces a perceived degree of positiveness (e.g., resounding vs. crushing victory).

combining their vectors produces clearly similar representations that confuse the classifier, a scenario which is only partly alleviated by the use of relation vectors (Fig.


All these verb variants (perfective, imperfective, reflexive) are presented as different entries in RuWordNet.

in most cases altogether they should easily overcome the established threshold of discrepancies.


For example, frequent Russian word zayavit (to proclaim) is often erroneously written as zavit (to curl).

the DBag of word zavit includes many words similar to zayavit such as soobshchit' (to inform), or otmetit (to remark).


We believe this to be the result of noise in the back-translated HRL, which is then compounded by further errors from the induced dictionary.

we suggest that the simple word substitution method should be preferred for the second pivoting step when augmenting back-translated HRL data.


The values M 3,i are converted to weights W of dimension [b, s, k, 1] using the formula W = max(0, 1 − M ) 3 , with the max operation ensuring the model puts a weight of zero on tokens in separate entities (see the reduction of the value of 1.7 in M in Figure 6 to a weighting of 0.0).

the weights are normalized to sum to 1, and multiplied with the unfolded token embeddings X to give the entity embeddings t , of dimension [b, s, e] the embeddings at the end of level 1 for the words "the", "United" and "Kingdom" (t 1 1 , t 1 2 and t 1 3 respectively) are all now close to equal, and all have been formed from a weighted average of the three separate token embeddings.


For the ACE corpus, the default metric in the literature Ju et al., 2018; does not include sequential ordering of nested entities (as many architectures do not have a concept of ordered nested outputs).

an entity is considered correct if it is present in the target labels, regardless of which layer the model predicts it on.


The OntoNotes labels include a named entity (TIME), in the second column, with the NP "twenty-four" minutes nested inside.

we take the model's prediction from the second column as our prediction for this entity.


We use the weights, W , (see Figure 6) to form the weighted average: In the case of the weighted average for the embedding tensor, R, we use the weights from the next level.

when updating, each token "sees" information from tokens/entities on other levels dependent on whether or not they are in the same entity.


Moreover, we can observe that DTAL with a limited amount of data (less than 6% of training data in all datasets), performs comparably to DL models with full training data.

we have demonstrated that a deep ER system with our transfer and active learning frameworks can provide a stable and reliable solu-  tion to entity resolution with low annotation effort.


However, we also see that semi-Markov SSVM produces more "larger predicted span" errors.

the recall of unit-length mentions is lower than Thresholded CRF.


It reveals the relevance of words to the main topics of the target text.

this paper aims to integrate human attention into keyphrase extraction models.


The modern-day eye tracking equipment resulting in a very rich and detailed dataset (Cop et al., 2017).

we utilize opensource eye-tracking corpora and do not require eye-tracking information of the target datasets.


(2016) , which prove the effectiveness of external knowledge.

this paper is in the line of integrating external knowledge into neural network models.


In our case, the input is not a natural language source sentence as in traditional machine translation; instead, the input x 1:S is a meaning representation, where each token x n is itself a tuple of attribute and value features, (f attr , f val ).

we represent a given input x 1:S as a sequence of attribute-value pairs from an input MR. For example, in the case of BASE MR [(attr=food, val=steak), (attr=food, val=chicken)], we would have , and x 2 =(f attr =food,f val =chicken).


We represent these additional features as a vector of additional supervision tokens or side constraints (Sennrich et al., 2016).

we construct a vector for each set of features, and concatenate them to the end of each attributevalue pair, encoding the full sequence as for BASE above.


We find counts of two types of common mistakes: deletions, where the model fails to realize a value from the input MR, and repetitions, where the model repeats the same value more than once.

9 we compute SER per MR as SER = D+R N , where D and R are the number of deletions and repetitions, and the N is the number of tuples in the MR, and average across the test outputs.


Different from previous works for chess commentary generation (Jhamtani et al., 2018;Kameko et al., 2015), we find all kinds of latent relationships in the current board vital for current move analysis.

our description model takes the representation of both b (0) and m (0) from the move encoder f M E as semantic contexts to produce description comment Y Desc .


This is not surprising-existing models represent the distribution over the entire vocabulary directly, whether they are common words, references to real world entities, or factual information like dates and numbers.

language models are unable to generate factually correct sentences, do not generalize to rare/unseen entities, and often omit rare tokens from the vocabulary (instead generating UN-KNOWN tokens).


Each entity is only mentioned a few times (less than 5 on average, with a long tail), and with more than thousand different relations.

it is clear that regular language models would not be able to generate factual text, and there is a need for language models to be able to refer to external sources of information.


After changing the birth date to 2013-03-21, the top three decoded tokens become "March", "21", "2013".

changing the fact in the knowledge graph directly leads to a corresponding change in the model's prediction.


Since the definition of the text style is vague, it is difficult to construct paired sentences with the same content and differing styles.

the studies of text style transfer focus on the unpaired transfer.


As reported in (Elazar and Goldberg, 2018;Lample et al., 2019), the style information can be still recovered from the latent representation even the model has trained adversarially.

it is not easy to disentangle the stylistic property from the semantics of a sentence.


4) To disentangle the content and style information in the latent space, all of the existing approaches have to assume the input sentence is encoded by a fix-sized latent vector.

these approaches can not directly apply the attention mechanism to enhance the ability to preserve the information in the input sentence.


Therefore the network can compute the probability of the output condition both on the input sentence x and the style control variable s. Formally, this can be expressed as: and we denote the predicted output sentence of this network by f θ (x, s).

suppose we use x and s to denote the sentence and its style from the dataset D. Because of the absence of the parallel corpora, we can't directly obtain the supervision for the case f θ (x, s) where s = s. we introduce a discriminator network to learn this supervision from the nonparallel copora.


Such word re-ordering knowledge is indeed learnable (Ma et al., 2018), and does not necessarily contain the syntactic information.

the multi-task and adversarial losses for syntax and semantics do not suffice to regularize DSS-VAE.


The intuition behind is that, when generating the paraphrase, semantics should remain the same, but the syntax of a paraphrase could (and should) vary.

we sample a z * syn vector from its probabilistic distribution, while fixing z * sem .


Second is the difficulty of explicitly controlling sentiment intensity when generating endings.

we propose a generic and novel framework which consists of a sentiment analyzer and a sentimental generator, respectively addressing the two challenges.


Meanwhile, all of them can largely outperform the Seq2Seq+SentiMod baseline which does not follow our framework.

it shows the effectiveness of the proposed framework.


Different from them, controlling the fine-grained sentiment (a numeric value) for story ending generation is not limited to several emotional labels, thus we can not embed each sentiment label into a separate vector as usual.

we propose to introduce the numeric sentiment value via a Gaussian Kernel Layer.


We hypothesize that this is a consequence of the relatively small size of the task-specific data.

in our experiments, we use word-level tokenization and GloVe (Pennington et al., 2014) as context-free pre-trained word vectors 5 .


For instance, two enti- ties such as Tesla and Edison could have close representations in the word embedding space, within a scientific-related subset of tokens: the use of a placeholder would thus prevent the use of such information.

we concatenate the contextfree vectors (see 3.2) for a specific token with the corresponding ELMO (Peters et al., 2018) representation at the encoding stage.


Here the answer reranking provides an effective remedy against the underlying noise-information trade-off due to a variable corpus size.

rankQA represents a novel, powerful, and thus challenging baseline for future research in content-based QA.


We analyze whether our system is capable of keeping the set of correctly answered questions after applying the re-ranking step.

we measure the fraction of correctly answered questions out of those questions that had been answered correctly before re-ranking.


The notable drop between development and test accuracy for SQuAD is a reflection of an artifact in the dataset-its 100k questions are derived from only 536 documents.

good retrieval targets are highly correlated between training examples, violating the IID assumption, and making it unsuitable for learned retrieval.


Since there  is no access to the groundtruth answers of multihop questions, a decomposition scorer cannot be trained.

a final answer is obtained based on the confidence score from the single-hop RC model, without a rescoring procedure.


The main contribution of this work is to tackle the WSC by: • developing and utilizing an automated knowledge hunting approach to extract the needed knowledge and reason with it without relying on a strict formal representation, • utilizing the knowledge that is embedded in the language models, and • combining the knowledge extracted from knowledge hunting and the knowledge in language models.

our approach improves on the existing state-of-the-art accuracy by 7.36% and solves 71.06% of the WSC problems correctly.


(A) sunlight (B) oxygen (C) water (D) carbon dioxide Conjunctive Reasoning: In the example as shown below, each answer options are partially correct as the word " bear" is present.

a model has to learn whether all parts of the answer are true or not, i.e Conjunctive Reasoning.


However, the output of the adapter is undecided, because there is no oracle representation for the relation detection task.

we first train a traditional relation detection model similar to Yu et al.


However, in a more realistic setting, the answers may even not reside in entities of the extracted entity graph.

existing approaches can hardly be directly applied to open-domain multi-hop QA tasks like HotpotQA.


By multiplying the soft mask and the initial entity embeddings, the desired start entities will be encouraged and others will be penalized.

this step of information propagation is restricted to a dynamic sub-part of the entity graph.


We set p(c|R; θ) to be the maximum of such proof scores: Note that the final proof score p(c|R; θ) only depends on the proof with maximum success score S max .

we propose to first conduct the proof search by using a prover utilizing the similarity function induced by the current parameters ∼ θt , which allows us to compute the maximum proof score S max .


While it is possible to write down rules involving natural language patterns, this approach does not scale.

we follow Rocktäschel and Riedel (2017) and use rule templates to perform Inductive Logic Programming (ILP) (Muggleton, 1991), which allows NLPROLOG to learn rules from training data.


The output of the group attention O is produced by: Following the same paradigm in (Vaswani et al., 2017), we add a fully-connected feed forward layer to the multi-head attention mechanism layer (i.e., group attention), and each layer is followed by a residual connection and layer normalization.

the output of group attention block O is obtained.


The question span can be viewed as the quantity span containing an unknown quantity.

the computation process consists of two parts: 1) Attention between quantities: the query Q p is derived from X quant,i , and corresponding K p and V p are stemmed from X quant,j (j = i).


Question-Related Attention: The question can also derive distinguishing information such as whether the answer value is positive.

we propose question-related attention, which is utilized to model the connections between question and problem description stem.


Especially, our model achieves 72.472% on F1 score only after the first epoch, which is about 10% to 20% higher than SDNet.

with fewer training epochs, our model still can perform well.


When a word omission error occurs, erroneous translations, which are mistakenly assigned with higher probabilities, are more likely to be chosen than ground-truth translations.

to reduce word omission errors, the probability that the NMT model assigns to an erroneous translation must be lower than that of a ground-truth translation.


Among them, attentive mechanism (Row 5) obtains the best performance in terms of BLEU score, while maintains the training and decoding speeds.

we used the attentive mechanism to implement the function GLOBAL(•) as the default setting in the following experiments.


Although spo- The Nation  150,724 80,596,156  6,232  The Guardian  73,894 39,411,837  59,232  The Punch  39,576 19,453,935  152,928  Vanguard  30,279 29,315,637  178,734  Daily Trust  29,019 14,481,549  723  BBC (Naijá)  6,999  1,114,844  n/a   Table 1: Corpus of Nigerian news in English and Naijá ken widely, no language detection systems support recognizing the creole, in part due to the lack of existing corpora with examples.

1 to support our ultimate goal of modeling the social factors influencing code switching, we first introduce a new corpus of Naijá and English texts and then develop a classifier to distinguish them.


As news media, all six datasets use a formal register in their style, which does not necessarily match that of the comments.

to supplement the news data, two annotators labeled a sample of 2,500 comments across all sites.


2016, we eliminate documents with summaries shorter than 50 words.

the NYT test set contains longer and more elaborate summary sentences than CNN/Daily Mail (see Table 1).


Recall that our decoder consists of three stages, each of which is a sequence-to-sequence model.

there is no guarantee that tree output will be well-formed.


When taking the derivative with respect to the loss, we treat the interpolated embeddings as constants.

the prior loss does not back-propagate to the embedding parameters.


During training, the model parameters are updated with respect to both losses.

the word embeddings had to adjust accordingly to the new model parameters by updating the embedding parameters with cross-entropy loss.


The attachment of sentences to concepts naturally dissects the original document into multiple disjoint sentence subsets.

we have represented the original document with a graph of key concepts, each with a sentence subset, as well as   Figure 2: An overview of our approach for constructing the Concept Interaction Graph (CIG) from a pair of documents and classifying it by Graph Convolutional Networks.


However, a number of breaking news articles emerge on the Internet everyday with their topics/themes unknown, so it is not possible to predefine their topics.

supervised text classification cannot be used here.


It is even impossible to determine how many news clusters there exist.

the task of classifying whether two news articles are reporting the same breaking news event or belong to the same story is critical to news apps and search engines for clustering, redundancy removal and topic summarization.


Note that we focus on the capability of long text matching.

we do not use any short text information, such as titles, in our approach or in any baselines.


Second, our model encodes local comparisons around different semantic units into local matching vectors, and aggregate them via graph convolutions, taking semantic topologies into consideration.

it solves the problem of matching documents via divide-and-conquer, which is suitable for handling long text.


However, our method achieves significantly better performance by using graphical decomposition.

we conclude that graphical decomposition can greatly improve long text matching performance.


However, the intrinsic structural properties of long text documents are not fully utilized by these neural models.

they cannot achieve good performance for long text pair matching.


the biggest benefit of using class weights is observed in categories where the number of instances during training is very low.


Even experienced investors may be confused, however, if the candidates are 6.9 and 7.

to test the numeracy of a model, we separate numerals into eight classes by the magnitude and ask models to predict a suitable range.


Besides, the influence of company names (Maersk Drilling and Eni) may be less than that of the key term.

we infer that the models can capture the main event in a market comment.